{"title":"关于自组织映射(SOMs)和随机邻居嵌入(SNE)的统一观点","authors":"Thibaut Kulak, Anthony Fillion, Franccois Blayo","doi":"10.48550/arXiv.2205.01492","DOIUrl":null,"url":null,"abstract":"We propose a unified view on two widely used data visualization techniques: Self-Organizing Maps (SOMs) and Stochastic Neighbor Embedding (SNE). We show that they can both be derived from a common mathematical framework. Leveraging this formulation, we propose to compare SOM and SNE quantitatively on two datasets, and discuss possible avenues for future work to take advantage of both approaches.","PeriodicalId":93416,"journal":{"name":"Artificial neural networks, ICANN : international conference ... proceedings. International Conference on Artificial Neural Networks (European Neural Network Society)","volume":"139 20","pages":"458-468"},"PeriodicalIF":0.0000,"publicationDate":"2022-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A unified view on Self-Organizing Maps (SOMs) and Stochastic Neighbor Embedding (SNE)\",\"authors\":\"Thibaut Kulak, Anthony Fillion, Franccois Blayo\",\"doi\":\"10.48550/arXiv.2205.01492\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We propose a unified view on two widely used data visualization techniques: Self-Organizing Maps (SOMs) and Stochastic Neighbor Embedding (SNE). We show that they can both be derived from a common mathematical framework. Leveraging this formulation, we propose to compare SOM and SNE quantitatively on two datasets, and discuss possible avenues for future work to take advantage of both approaches.\",\"PeriodicalId\":93416,\"journal\":{\"name\":\"Artificial neural networks, ICANN : international conference ... proceedings. International Conference on Artificial Neural Networks (European Neural Network Society)\",\"volume\":\"139 20\",\"pages\":\"458-468\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-05-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Artificial neural networks, ICANN : international conference ... proceedings. International Conference on Artificial Neural Networks (European Neural Network Society)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.48550/arXiv.2205.01492\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Artificial neural networks, ICANN : international conference ... proceedings. International Conference on Artificial Neural Networks (European Neural Network Society)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.48550/arXiv.2205.01492","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A unified view on Self-Organizing Maps (SOMs) and Stochastic Neighbor Embedding (SNE)
We propose a unified view on two widely used data visualization techniques: Self-Organizing Maps (SOMs) and Stochastic Neighbor Embedding (SNE). We show that they can both be derived from a common mathematical framework. Leveraging this formulation, we propose to compare SOM and SNE quantitatively on two datasets, and discuss possible avenues for future work to take advantage of both approaches.
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